Editorial Feature

AI-Powered Material Handling Robots

We discuss how AI-powered robots revolutionize traditional manufacturing by enhancing efficiency, precision, and adaptability, offering insights into their applications, benefits, and the role of AI in optimizing their performance. 

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In the manufacturing sector, robots have been employed to automate tasks and accelerate work progress [1]. AI-powered robotics can enable robots to learn and become intelligent, similar to humans, potentially revolutionizing traditional manufacturing. While robots inherently only act as per the instructions embedded into their framework, AI-enhanced robotics, consisting of efficient sensors, and leveraged by data-driven computation can augment their manufacturing capabilities [2].

The Use of AI in Manufacturing Robots

AI-powered robots can consistently handle repetitive and monotonous manufacturing tasks throughout shifts, enhancing production efficiency and lowering labor expenses [1]. Incorporating neural networks allows manufacturing robots to continually learn and enhance their functionality. As a result, robots can perform manufacturing tasks with millimeter precision, which is particularly crucial for industries like aerospace and automotive.

AI-powered manufacturing robots are well-suited for operation in challenging environments [1]. These robots continuously monitor critical parameters such as temperature, humidity, and emission levels as they are with modern sensors featuring advanced data processing capabilities.

In manufacturing assemblies, AI-driven robots automate production lines, handle product assembly, manage quality control, and oversee logistics. Their capability to operate continuously without fatigue and with precise accuracy has revolutionized the production of consumer goods [2].

AI-driven industrial robots have greatly transformed the manufacturing industry worldwide, as per the article published in Production Planning & Control [7]. Particularly prevalent in the automotive sector, these robots accelerate manufacturing processes, enhance productivity, and make research and development activities more efficient [7]. AI-empowered robots contribute to optimizing the production of engine parts, chassis, body components, electrical parts, suspension and braking elements, as well as steering and drive transmission parts for automobile companies.

What Are Material Handling Robots?

In manufacturing industries, substantial effort is invested in material handling tasks. This has led to a notable increase in the demand for material handling products to minimize efforts, reduce time, and cut costs in this activity. Traditionally, material-handling products have been developed using conventional manipulation technologies to ease the physical strain on workers involved in lifting and moving materials.

However, manual handling systems lack intuitive and responsive control, leading to issues like back discomfort. Ongoing industry trends, including shorter product lifecycles, quicker time-to-market, and mass customization, necessitate novel paradigms and approaches in handling technology as per the article published in ISARC: Proceedings of the International Symposium on Automation and Robotics in Construction [8].

For this purpose, specialized robots known as material handling robots are being utilized. Material handling robots offer automation for some of the most tedious, monotonous, and unsafe tasks on a production line, representing one of the simplest ways to introduce automation [3]. These robots improve production line efficiency and boost customer satisfaction by delivering quality products promptly.

Automated material handling robotics applications offer advantages beyond just alleviating strain on workers and preventing injuries [3]. Despite not immediately apparent, the return on investment (ROI) of automated material handling applications with industrial robots is substantial.

Applications of Material Handling Robots

Material-handling robots have a diverse range of applications in different industries [4]. Proficient in tasks like automated transportation of parts or material units from a warehouse to an operator, these robots execute "Goods-to-person picking," minimizing unnecessary operator movements and eliminating unproductive time [1].

In production lines, where missing components can disrupt manufacturing, material handling robots ensure a seamless supply to each stage, allowing operators to proceed without interruptions [4].

The accuracy of sorting applications performed by material handling robots relies on advanced software development, making it a complex task to automate. However, recent technological advancements have reached a level that ensures efficiency and reliability in these operations.

Role of Artificial Intelligence in Improving the Performance of Material Handling Robots

AI and machine learning play a crucial role in enhancing material-handling robot capability by facilitating intelligent decision-making, predictive maintenance, and adaptive control of equipment [5]. These technologies contribute to optimizing system performance and adjusting to real-time changes in conditions.

Autonomous vehicles and picking robots are prominent examples of AI applications in material handling [6]. ML algorithms are integrated into the software of material-handling robots, empowering them to handle decision-making tasks such as navigation path planning and collision avoidance.

The autonomy levels of these systems continually advance with evolving technology. While fixed-path autonomous guided vehicles (AGVs) have gained popularity in the global material handling sector, the evolution of autonomous mobile robots (AMRs) further enhances efficiency [1].

AMRs can navigate warehouses without relying on fixed infrastructure and adapt to path changes using specialized AI-based algorithms, such as image processing algorithms.

Natural Language Processing (NLP) is a component of what IBM terms "cognitive automation," representing the convergence of AI with robotic process automation (RPA) software for intelligent and autonomous handling of back-office administration tasks [6].

NLP is instrumental in automating tasks related to documentation and paperwork, enabling the interpretation and utilization of "unstructured" data in text documents by a computerized system. It also allows material-handling robots to interact with workers smoothly, improving the communication between humans and robots using natural language commands.

The adoption of material-handling robots has resulted in substantial savings in both finances and time. AI implementation allows robots to continuously learn and gather data, conducting analyses to enhance performance. This integration of AI is pivotal in transforming robots into truly autonomous entities.

See More: Training Artificial Intelligence in Industry through Machine Learning Algorithms

References and Further Reading

[1] Robotnik, (2023). "The future is here: Artificial Intelligence robots transforming industries," [Online]. Available at: https://robotnik.eu/the-future-is-here-artificial-intelligence-robots-transforming-industries/. [Accessed 6 January 2024].

[2] C. Vavra. (2023). "Industrial robots powered by AI improve manufacturing," [Online]. Available at: https://www.controleng.com/articles/industrial-robots-powered-by-ai-improve-manufacturing/

[3] Robots-A TIE Brand (2023) "Material Handling Robots," 2023. [Online]. Available at: https://www.robots.com/applications/material-handling

[4] Engra, (2022). "Application of robots for material handling," 2022. [Online]. Available at: https://robotnik.eu/what-are-handling-robots-robots-for-material-handling/

[5] S. Malik (2023). "Material Handling Automation: A Comprehensive Guide for Engineers," 2023. [Online]. Available at: https://www.wevolver.com/article/material-handling-automation-a-comprehensive-guide-for-engineers. [Accessed 8 January 2024].

[6] Otto Motors (2023). "5 AI Technologies and Their Application in Material Handling," 2023. [Online]. Available at: https://ottomotors.com/blog/5-ai-technologies-in-materials-handling

Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.

Ibtisam Abbasi

Written by

Ibtisam Abbasi

Ibtisam graduated from the Institute of Space Technology, Islamabad with a B.S. in Aerospace Engineering. During his academic career, he has worked on several research projects and has successfully managed several co-curricular events such as the International World Space Week and the International Conference on Aerospace Engineering. Having won an English prose competition during his undergraduate degree, Ibtisam has always been keenly interested in research, writing, and editing. Soon after his graduation, he joined AzoNetwork as a freelancer to sharpen his skills. Ibtisam loves to travel, especially visiting the countryside. He has always been a sports fan and loves to watch tennis, soccer, and cricket. Born in Pakistan, Ibtisam one day hopes to travel all over the world.


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